Today, using AI is not as daunting as it was even a few years ago. Powerful models are becoming easier to implement all the time. According to a 2024 McKinsey report, the number of businesses that use GenAI in at least one business function nearly doubled this year to 65%.¹ The majority of organizations (91%) polled in a 2024 survey by Deloitte expect GenAI applications to increase their productivity, with IT, cybersecurity, marketing, customer service, and product development among the most impacted areas.²

The same Deloitte report goes further to say that less than half (47%) of the organizations surveyed reported that they are sufficiently educating their employees on the capabilities, benefits, and value of generative AI. Survey respondents also cited a lack of technical talent and skills as the most significant barriers to adoption.³ 

This blog lets us understand the engine that fuels the fervor of generative AI (GenAI). large language models (LLMs). Large language models (LLMs) are statistical models used to predict the next words in a sequence of natural language.^4,5 They are designed to process and analyze natural language data, which is only one aspect—albeit a vital AI subfield—of the more extensive GenAI umbrella. LLMs fall into the category of large foundation models (LFMs), or base models, a broader set of models we discussed in the previous post in this blog series (Fig. 1).⁶

Figure 1: Relationship between AI, Deep Learning, Machine Learning, and GenAI

Understanding the inner workings of LLMs requires a basic grasp of the underlying mathematical principles that power these systems. Although the actual computations can be complex, we can simplify the core elements to provide an intuitive understanding of how these models operate. LLM is a compelling application of Deep Learning (DL) that enables machines to be trained on vast amounts of text data without being explicitly programmed for every search and without requiring training data that is task specific. The power of the LLM is expressed in its ability to craft coherent and contextually relevant sentences, creating novel content that closely mimics human-created work. LLMs are adapted to process, understand, and generate human language, facilitating advanced interactions between humans and machines across various industry verticals. 

At its core, a language model (LM) is a probabilistic model that learns to predict the next word (or token) in a sequence, based on the preceding or surrounding words. The text generation process in LLMs is autoregressive, meaning they generate the next word based on the sequence of words already generated. The attention mechanism that we will introduce in a future post in this blog series (familiarity with the terms is a good start at this point) is a vital component in this process; it establishes word relations and ensures the text is coherent and contextually appropriate.⁷

Particularly within a business context, ensuring the accuracy and reliability of LLMs is paramount. A significant factor in achieving accuracy and reliability lies in the pretraining and fine-tuning phases of LLM development. Initially, models are trained on vast datasets during the pretraining phase, acquiring a broad understanding of language. Subsequently, in the fine-tuning phase, models are adapted for specific tasks, honing their ability to provide accurate and reliable outputs for specialized applications.

Training LLMs requires extensive computational resources. These models are fed vast amounts of data, ranging from terabytes to petabytes, including internet content, academic papers, books, and niche datasets tailored for specific purposes. LLMs are capable of powering diverse applications, from writing content to automating software development and enabling real-time interactive chatbot experiences.

Strong LLMs like GPT 4 or Claude 3 function much like a polymath who works tirelessly without demanding compensation (beyond subscription or API fees), providing competent assistance in subjects like mathematics and statistics, macroeconomics, biology, and law (the model performs well on the Uniform Bar Exam). Major technology companies such as Microsoft, Meta, and Google have invested heavily to develop these capabilities, making LLM development a high-stakes endeavor. As these AI models become more proficient and easily accessible, they will likely play a significant role in shaping the future of work and learning.

By making knowledge more accessible and adaptable, LLMs have the potential to level the playing field and create new opportunities for people from all walks of life. These models have shown potential in areas requiring high levels of reasoning and understanding, although progress varies depending on the complexity of the task at hand.

Embracing GenAI in business means being open to radical change, questioning existing business processes without fear of disrupting the status quo, and being dauntless in throwing out the rulebook and starting anew to achieve better business outcomes. Trailblazers, innovators, and those who are curious and on the lookout for technological developments that lie around the corner will reap the greatest benefit from GenAI. AI will not replace the role of humans in critical functions, but those incapable of embracing AI technologies  will find themselves at a disadvantage, unable to partner and collaborate with AI practitioners within their organizations and beyond.

References:

1. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
2. https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-state-of-gen-ai-report.pdf
3. https://learning.oreilly.com/library/view/generative-ai-with/9781835083468/Text/Chapter_1.xhtml#_idParaDest-18
4. Generative AI with LangChain | Data | Print. https://www.packtpub.com/product/generative-ai-with-langchain/9781835083468
5. https://learning.oreilly.com/library/view/building-llms-for/9798324731472/index_split_009.html#id_keyllmterminologies
6. https://dataworksai.com/embracing-genai-for-business-success-part-ii/
7. https://wp.technologyreview.com/wp-content/uploads/2024/11/MITTR_Redis_final_26nov24.pdf